Y. Goto, H. Morita, Y. Shirai, and H. Ichikawa;
“Simulation-based classification in multi-objective optimization problems with social simulation”
It would be nice if we could draw some insight on the general tradeoffs faced across scenarios
Generate the pareto fronts, then let’s cluster them somehow.
How do you cluster pareto fronts?
Thomas Chesney, “A philosophy of intelligent agent-based models”
We can use reinforcement learning with neural networks to teach cars how to behave
For the right amount of sophistication, they eventually learn better behaviour.
The problem is, what does a traffic model with super-smart agents actually means?
ABMs are often not operational puzzles to maximize; agent behaviour needs to be proportional to the problem at hand.
Even though we can, often we shouldn’t.
Lux Miranda; “Evolutionary model discovery of human behavioral factors driving decision-making in irrigation experiments”
Instead of writing a utility function and calibrating, let’s use genetic programming to evolve the utility function itself.
It works as a gentle introduction to inverse generativve social science.
When the problem and functions are this simple however, IGS is really a “calibration with genetic programming”
Edmund Chattoe-Brown , “All the right moves? Systematically exploring the effects of random travel in agent-based models”
Not the only way to be random; this method produces “line of sight” explorations.
“Movement” in the model is a complicated proxy for habitat and exploration. Without estimation/calibration, every component of an abstract model matters.
Alison Heppenstall; “Simulating social systems with individual-based models: are they worth it?”
Deborah Olukan, Jonathan Ward, Nicolas Malleson & Jiaqi Ge; “Heterogeneity in agent-based models”
Joel Dyer; “Black-box Bayesian inference for agent-based models”